AI and Quantum Computing: Myth or reality?

Nowadays, there is buzz about how Quantum Computing will revolutionize many domains. We wish it would be true for Artificial Intelligence (AI), but in this case, it is more myth than reality.

There has actually been impressive progress in recent years to build quantum computers. However, it is important to remember that no future quantum computer can exceed the theoretical limits of quantum computing. Similar to math, in quantum computing, theory dictates what is feasible in practice. At this point, researchers in quantum computing have a clear and thorough theoretical understanding of what quantum computing can/cannot do, irrespective of whether quantum computers have been built or not.

It turns out that for the vast majority of computing problems, quantum computers are theoretically no faster than classical computers used today. The main potential of quantum computing resides in cryptography (i.e. security) and modeling quantum states in materials (e.g. for material sciences, pharmaceuticals), none of which involves AI or Machine Learning (the most popular AI technique these days).

BIG Expert Thomas Dillig giving a talk on Quantum Computing for senior business and military leaders at a private seminar organized by the French-American Foundation

Machine Learning (ML) uses many different kinds of computing building blocks (that most people have never heard about) such as vector multiply and gradient descent. Theoretically, some computing building blocks used in training ML algorithms could perform faster with quantum computing. However, this would only be feasible and useful, if reliable and cost-effective quantum computers can be built – which is not the case today.

Even then, the performance of many other computing building blocks used in ML cannot be increased by any type of quantum computing – limiting the overall impact of quantum computing in this domain. To improve AI performance in the future, there is much greater, practical potential from specialized AI processors based on classical computing techniques and used with classical computers. Such AI-accelerator chips are already becoming available from major manufacturers like NVidia and Intel.